Import climaf¶
In [1]:
from climaf.api import *
clog('critical') # min verbosity = critical < warning < info < debug = max verbosity
python => 3.10.13 | packaged by conda-forge | (main, Oct 26 2023, 18:07:37) [GCC 12.3.0] --- Required softwares to run CliMAF => you are using the following versions/installations:
CliMAF climaf_version = 3.0
CliMAF install => /home/ssenesi/climaf_installs/climaf_running
Cache directory set to : /data/ssenesi/climaf_cache (use $CLIMAF_CACHE if set)
Cache directory for remote data set to : /data/ssenesi/climaf_cache/remote_data (use $CLIMAF_REMOTE_CACHE if set)
warning : defining curl_tau_atm : command ferret is not executable
warning : Binary cdftools not found. Some operators won't work
Available macros read from ~/.climaf.macros are : []
error : second argument ('curl_tau_atm') must be a script or operator, already declared
In [2]:
# When developing the script of a CliMAF operator, we may wish to systematically
# recompute the object we look at
# Redefining function cshow allows for that
def cshow(obj):
climaf.api.cshow(obj, drop=True)
0. Get a dataset¶
In [3]:
dat = ds(project='CMIP5', model='CNRM-CM5', experiment='historical', frequency='monthly', version='v20130101',
period='1980-1982', variable='tos', realm='ocean' )
#dat = fds("/bdd/CMIP5/output/CNRM-CERFACS/CNRM-CM5/historical/mon/ocean/Omon/r1i1p1/v20130101/tos/tos_Omon_CNRM-CM5_historical_r1i1p1_198001-198912.nc")
summary(dat)
/bdd/CMIP5/output/CNRM-CERFACS/CNRM-CM5/historical/mon/ocean/Omon/r1i1p1/v20130101/tos/tos_Omon_CNRM-CM5_historical_r1i1p1_198001-198912.nc
Out[3]:
{'project': 'CMIP5',
'simulation': '',
'variable': 'tos',
'period': 1980-1982,
'domain': 'global',
'root': '/bdd',
'model': 'CNRM-CM5',
'table': '*',
'experiment': 'historical',
'realization': 'r1i1p1',
'frequency': 'monthly',
'realm': 'ocean',
'version': 'v20130101'}
In [4]:
myplot = plotmap(time_average(dat))
cshow(myplot)
In [5]:
myplot = plotmap(time_average(dat),
title='My CliMAF plot',
title_options={
'lefttitle':dat.variable + ' / ' + str(dat.period),
'righttitle':dat.model},
)
cshow(myplot)
In [6]:
myplot = plotmap(time_average(dat),
title='My CliMAF plot',
title_options={
'maintitlefontsize':30,
'lefttitle':dat.variable+ ' / ' + str(dat.period),
'righttitle':dat.model,
'righttitlefontsize': 9,
}
)
cshow(myplot)
Size of the Axis labels, colorbar labels, the fonts: see http://climaf.readthedocs.org/en/dev/scripts/plotmap.html¶
Give it a thought ....
In [7]:
myplot = plotmap(time_average(dat),title='My CliMAF plot',
# -- Colorpalette
color='WhBlGrYeRe',
# -- Add contours
contours=1,
# -- Add an offset to switch from K to degC
offset = -273.15, units = "C",
# -- Range and spacing
min=-1, max=30, delta=1,
)
cshow(myplot)
In [8]:
myplot = plotmap(time_average(dat),title='My CliMAF plot',
# -- Colorpalette
color='WhBlGrYeRe',
# -- Add contours
contours=1,
# -- Add an offset to switch from K to degC
offset = -273.15, units = "C",
# -- Range and spacing
min=-1, max=30, delta=1,
# Shade land with grey
focus = 'ocean',
# Change central longitude
proj = 'PlateCarree',
proj_options = {'central_longitude' : 0}
)
cshow(myplot)
In [9]:
# --> Interpolate to coarser grid to see the raster
rdat=regridn(time_average(dat),cdogrid='r90x45')
myplot = plotmap(rdat,
# -- Title
title='Raster plot with CliMAF',
# -- Colorpalette
color='WhViBlGrYeOrRe',
# -- Add an offset to switch from K to degC
offset = -273.15,
# -- Color ranges
min=-1,max=30,delta=1,
# -- Grey on lands
focus='ocean',
# -- Change cartopy/matplotlib plot engine
clre='pcolormesh'
)
cshow(myplot)
In [10]:
# --> Set proj to 'NH' or 'SH'; the two additionnal characters set the last latitude visible
projNH = 'NH20'
myplotNH = plotmap(regridn(clim_average(dat,'ANM'),cdogrid='r360x180'),
# -- Title
title=projNH+' plot',
# -- Colorpalette
color='WhViBlGrYeOrRe',
# -- And add an offset to switch from K to degC
offset = -273.15,
# -- Add contours
contours=1,
# -- Isolines
min=-1,max=30,delta=1,
# -- Grey on lands
focus='ocean',
# -- Northern Hemisphere projection
proj=projNH,
)
projSH = 'SH60'
myplotSH = plotmap(regridn(clim_average(dat,'ANM'),cdogrid='r360x180'),
# -- Title
title=projSH+' plot',
# -- Colorpalette
color='WhViBlGrYeOrRe',
# -- And add an offset to switch from K to degC
offset = -273.15,
# -- Add contours
contours=1,
# -- Isolines
min=-1,max=30,delta=1,
# -- Grey on lands
focus='ocean',
# -- Northern Hemisphere projection
proj=projSH
)
#cdrop(myplotNH); cdrop(myplotSH); cdrop(cpage(fig_lines=[[myplotNH,myplotSH]]))
iplot(cpage(fig_lines=[[myplotNH,myplotSH]]))
Out[10]:
In [11]:
# -- Get an auxillary field
aux = ds(project='CMIP5', model='CNRM-CM5', experiment='historical', frequency='monthly', version='v20130101',
period='1980-2005', variable='sic')
summary(aux)
/bdd/CMIP5/output/CNRM-CERFACS/CNRM-CM5/historical/mon/seaIce/OImon/r1i1p1/v20130101/sic/sic_OImon_CNRM-CM5_historical_r1i1p1_185001-200512.nc
Out[11]:
{'project': 'CMIP5',
'simulation': '',
'variable': 'sic',
'period': 1980-2005,
'domain': 'global',
'root': '/bdd',
'model': 'CNRM-CM5',
'table': '*',
'experiment': 'historical',
'realization': 'r1i1p1',
'frequency': 'monthly',
'realm': '*',
'version': 'v20130101'}
In [12]:
# --> Provide the auxillary field as a second argument
myplotNH = plotmap(time_average(dat),
# -- Field for contours is provided as a second argument
time_average(aux),
# -- Specifiy contours (we can also use a python list)
contours='1 5 10 15',
# -- Handle options for the contoured field
cnteo={'colors':'red','linewidths':2},
# -- Title
title='SST + Sea Ice contours',
# -- Colorpalette
color='WhViBlGrYeOrRe',
# -- And add an offset to switch from K to degC
offset = -273.15,
# -- Color ranges
min=-1,max=30,delta=1,
# -- Grey on lands
focus='ocean',
# -- Northern Hemisphere projection
proj='NH50',
#proj_options={'central_longitude':45}
)
cshow(myplotNH)
In [13]:
tauu = ds(project='CMIP5', model='CNRM-CM5', experiment='historical', frequency='monthly', version='v20130101',
period='1980-2005', variable='tauuo')
tauv = ds(project='CMIP5', model='CNRM-CM5', experiment='historical', frequency='monthly', version='v20130101',
period='1980-2005', variable='tauvo')
summary(tauv)
/bdd/CMIP5/output/CNRM-CERFACS/CNRM-CM5/historical/mon/ocean/Omon/r1i1p1/v20130101/tauvo/tauvo_Omon_CNRM-CM5_historical_r1i1p1_198001-198912.nc /bdd/CMIP5/output/CNRM-CERFACS/CNRM-CM5/historical/mon/ocean/Omon/r1i1p1/v20130101/tauvo/tauvo_Omon_CNRM-CM5_historical_r1i1p1_199001-199912.nc /bdd/CMIP5/output/CNRM-CERFACS/CNRM-CM5/historical/mon/ocean/Omon/r1i1p1/v20130101/tauvo/tauvo_Omon_CNRM-CM5_historical_r1i1p1_200001-200512.nc
Out[13]:
{'project': 'CMIP5',
'simulation': '',
'variable': 'tauvo',
'period': 1980-2005,
'domain': 'global',
'root': '/bdd',
'model': 'CNRM-CM5',
'table': '*',
'experiment': 'historical',
'realization': 'r1i1p1',
'frequency': 'monthly',
'realm': '*',
'version': 'v20130101'}
In [14]:
myplot = plotmap(regridn(time_average(dat),cdogrid='r360x180'),
# -- Provide dummy second argument (i.e. no field to contour)
"",
# Vector components are third and fourth arguments
time_average(tauu),time_average(tauv),
# Vectors graphic settings
veco = {'color':'white',
# A vector of 1 m/s will be 1/0.5 inches long
'scale':0.5, 'scale_units':'inches',
# dimensions of arrow shaft and arrow head
'units':'dots', 'width':1, 'headwidth':5
# See also https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.quiver.html
},
# How many vectors along each axis
vecg = [60 , 30],
# -- Title
title='My CliMAF plot',
# -- Colorpalette
color='WhViBlGrYeOrRe',
# -- Add contours
contours=1,
# -- And add an offset to switch from K to degC
offset = -273.15, units="degC",
# -- Color ranges
min=-1, max=30, delta=1,
# -- Grey on lands
focus='ocean'
)
cshow(myplot)
In [21]:
field = regridn(time_average(dat),cdogrid='r360x180')
field_with_ones_above_298 = ccdo(field, operator = "gec,298")
myplot = plotmap(field,
# Dummy values for contour and vector fields
None, None, None,
# Field for the pattern-shading (hatching)
field,
# -- Add an offset to switch from K to degC
offset = -273.15,
# -- Add the offset also to the pattern-shaded field
shdo = -273.15,
# Levels defining intervals which are patterned-filled
shdl = [ 25, 40],
# Pattern values for corresponding intervals. Character repetition drives density
shdh = ['...'],
# - Title
title='My CliMAF plot',
# -- Colorpalette
color='WhViBlGrYeOrRe',
# -- Color ranges
min=-1,max=30,delta=1,
# -- Grey on lands
focus='ocean'
)
cshow(myplot)
In [22]:
rgrd_dat = regridn(time_average(dat),cdogrid='r360x180')
myplot = plotmap(llbox(rgrd_dat,lonmin=-90,lonmax=20,latmin=0,latmax=90),
title='North Atlantic',
# Need to set a central_longitude so that region is not split
proj_options={'central_longitude':0})
cshow(myplot)
11. Various fancy projections available with proj¶
see all projections here: https://scitools.org.uk/cartopy/docs/latest/reference/crs.html
In [25]:
field = time_average(dat)
proj = 'PlateCarree'
p1 = plotmap(field, title=proj, proj=proj)
proj = 'Robinson'
p2 = plotmap(field, title=proj, proj=proj)
proj = 'Mollweide'
p3 = plotmap(field, title=proj, proj=proj)
proj = 'Orthographic'
p4 = plotmap(field, title=proj, proj=proj)
#cdrop(p1) ; cdrop(p2) ; cdrop(p3) ; cdrop(p4) ; cdrop(cpage(fig_lines = [[p1,p2],[p3,p4]]))
iplot(cpage(fig_lines = [[p1,p2],[p3,p4]]))
Out[25]:
13.1 : Satellite projection¶
In [26]:
p1 = plotmap(field, proj='NearsidePerspective',
title='36 * 1e6',
proj_options=dict(central_longitude=-40, central_latitude=45, satellite_height=36*1.e6)
)
p2 = plotmap(field, proj='NearsidePerspective',
title='height 36 * 1.e5',
proj_options=dict(central_longitude=-40, central_latitude=45, satellite_height=36*1.e5)
)
p3 = plotmap(field, proj='NearsidePerspective',
title='height 36 * 1.e4',
proj_options=dict(central_longitude=-40, central_latitude=45, satellite_height=36*1.e4)
)
p4 = plotmap(field, proj='NearsidePerspective',
title='lon 130, lat -25',
proj_options=dict(central_longitude=130, central_latitude=-25, satellite_height=36*1.e6)
)
p5 = plotmap(field, proj='NearsidePerspective',
title='lon 295, lat -10',
proj_options=dict(central_longitude=295, central_latitude=-10, satellite_height=36*1.e6)
)
#cdrop(p1); cdrop(p2); cdrop(p3); cdrop(p4); cdrop(p5); cdrop(cpage(fig_lines=[[p1,p2,p3],[p4,p5,None]]))
iplot(cpage(fig_lines=[[p1,p2,p3],[p4,p5,None]]))
Out[26]:
